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Phd Position F/m Towards a Comprehensive Speech

Il y a 3 mois


Strasbourg, France Inria Temps plein

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Doctorant

**Contexte et atouts du poste**:
Cette offre s'inscrit dans le cadre du projet ANR SpeechPrivacy, qui vise à faire progresser les technologies d'anonymisation de la parole. Le doctorant sera co-encadré par Emmanuel Vincent et Mickaël Rouvier. Il/elle aura l'opportunité de passer du temps à la fois dans Multispeech et au LIA, et de contribuer aux objectifs généraux du projet, y compris l'organisation d'une campagne d'évaluation internationale [1].

**Mission confiée**:
La collecte, le stockage et le traitement à grande échelle des données vocales posent de graves menaces pour la vie privée [2]. En effet, la parole renferme une multitude d'informations personnelles dans les traits du locuteur et dans le contenu verbal et non verbal (par exemple, l'identité du locuteur, son âge et son sexe, son origine ethnique, ses traits de personnalité, son état de santé et son statut socio-économique, etc.). Le respect des lois sur la protection des données telles que le RGPD nécessite des solutions de préservation de la vie privée adaptées aux technologies vocales. Cela a conduit au développement de méthodes d'anonymisation de la voix [3] qui visent à dissimuler la voix du locuteur dans la parole verbale et de méthodes d'évaluation [1] qui quantifient le risque de réidentification du locuteur par des attaques de corrélation et d'individualisation [4] sous des hypothèses simplificatrices. Cette thèse vise à étendre ces méthodes à un cadre complet d'anonymisation de la parole, qui quantifie le risque réel et prend en compte la parole non verbale et d'autres traits du locuteur.

[2] A. Nautsch, A. Jimenez, A. Treiber, J. Kolberg, C. Jasserand, E. Kindt, H. Delgado, M. Todisco, M. A. Hmani, M. A. Mtibaa, A. Abdelraheem, A. Abad, F. Teixeira, M. Gomez-Barrero, D. Petrovska, N. Chollet, G. Evans, T. Schneider, J.F. Bonastre, B. Raj, I. Trancoso, and C. Busch, “Preserving privacy in speaker and speech characterisation,” Computer Speech and Language, vol. 58, pp. 441-480, 2019.

[3] B. M. L. Srivastava, M. Maouche, M. Sahidullah, E. Vincent, A. Bellet, M. Tommasi, N. Tomashenko, X. Wang, and J. Yamagishi, “Privacy and utility of x-vector based speaker anonymization,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 2383-2395, 2022.

**Principales activités**:
Les mesures existantes d'évaluation au cas moyen surestiment le risque de réidentification en raison des hypothèses irréalistes que l'attaquant est capable de fixer le seuil de décision optimal et sait quand la réidentification a réussi. En même temps, ces mesures sous-estiment le risque pour certains locuteurs ou phrases plus facilement réidentifiables [5, p.105]. Un premier objectif est donc de développer des mesures de fiabilité [6] permettant à l'attaquant de décider si la réidentification a réussi, et d'étudier ce qui rend certains locuteurs ou phrases plus facilement réidentifiables afin d'adapter les paramètres d'anonymisation associés sans effectuer d'expériences coûteuses de vérification du locuteur.

Les méthodes d'anonymisation et d'évaluation existantes ne tiennent pas non plus compte de la parole non verbale (rire, respiration, toux, cri, pleurs, etc.) et des caractéristiques du locuteur (âge, sexe, phonation, articulation, prosodie, accent, etc.). Bien que des classifieurs aient été développés pour plusieurs de ces traits [7,8], leur anonymisation et leur utilisation pour des attaques par inférence [4] restent à étudier. Un deuxième objectif sera donc d’étudier l’efficacité de transformations simples de la parole [9] ou de techniques de synthèse spécifiques [10] pour l’anonymisation de la parole non verbale, et de quantifier le risque de ré-identification induit par la combinaison de plusieurs traits du locuteur.

[5] B. M. L. Srivastava, “Speaker anonymization : representation, evaluation and formal guarantees,” PhD thesis, University of Lille, 2021.

[6] P.M. Bousquet, M. Rouvier, and J.F. Bonastre, “Reliability criterion based on learning-phase entropy for speaker recognition with neural network,” in Interspeech, pp. 281-285, 2022.

[7] Y. Lin, X. Qin, N. Jiang, G. Zhao, and M. Li, “Haha-Pod: An attempt for laughter-based non-verbal speaker verification,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop, pp. 1-7, 2023.

[8] N. Obin and A. Roebel, “Similarity search of acted voices for automatic voice casting,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no.9, pp. 1642-1651, 2016.

[9] J. Patino, N. Tomashenko, M. Todisco, A. Nautsch, and N. Evans, “Speaker anonymisation using the McAdams coefficient,” in Interspeech, pp. 1099-1103, 2021.

[10] D. Xin, S. Takamichi, A. Morimatsu, and H. Saruwatari, “Laughter synthesis using pseudo phonetic tokens with a large-scale